Social Network Analysis Workshop

1. ep.jhu.edu 11100 Johns Hopkins Road Laurel, MD 20723-6099 Social Network Analysis (SNA) 15 May 2018 Ian McCulloh, Ph.D. Parson Fellow, Bloomberg School of Public Health Senior Lecturer, Whiting School of Engineering Senior Scientist, Applied Physics Laboratory imccull4@jhu.edu 1

2. ep.jhu.eduep.jhu.edu 1. PhD Computer Science/Social Networks, Carnegie Mellon University. 2. 50+ peer reviewed papers. 3. Author of Wiley’s textbook on Social Network Analysis. 4. Current research: social media and the neuroscience of persuasion. 5. 20 years in the US Army – Network targeting. 6. Organizing the North American Social Network (NASN) Conference in DC 27-30 NOV 2018 Background

3. ep.jhu.eduep.jhu.edu Getting Started https://www.rstudio.com https://ep.jhu.edu/programs-and-courses •605.633—Social Media Analytics •605.634—Crowdsourcing and Human Computation •605.632—Graph Analytics

4. ep.jhu.eduep.jhu.edu • Study of sociology • Organizational behavior (leadership, management) • Influencing groups (public health, propaganda, marketing) • Increasing engagement with social media (computer science) • Cool algorithms/heuristics (math, computer science) Why Social Networks?

6. ep.jhu.eduep.jhu.edu Study of Sociology Moreno (1934) Sociometry • 2nd grade classroom in the US • Social physics • The birth of social networks Network Science • 1999 Albert-Barabasi Scale-free networks • 2004 NRC Report, Army interest • Often lacks empirically grounded social theory that has been developed over 65years – communities are merging

8. ep.jhu.eduep.jhu.edu Organizational Behavior • Who is the most powerful person in this network?

9. ep.jhu.eduep.jhu.edu Organizational Behavior

10. ep.jhu.edu Organizational Spectrum 23 May 1 Agility Efficiency • Well-defined task/purpose • High repetition • Standards/quality control • Reduce waste/minimize costs • Hierarchy/supervision • Unity of leadership • Social interaction = distraction • Pioneering/no defined task • High novelty/not done before • Creative/diversity of ideas • Innovation/maximize new ideas • Organic/collaboration • Flat structure/many bosses • Social interaction = value

11. ep.jhu.edu Measuring Organizational Efficiency 1 Four Properties: 1. Connected 2. Hierarchic (no reciprocity) 3. Efficient (no cross-talk) 4. Least Upper Bound 1 − 𝑉 𝑛 𝑛 − 1 /2 1 − 𝑉 max 𝑉 1 − 𝐿 max 𝐿 1 − 𝑈 max 𝑈 V = reciprocal link L = #links above nk-1 U = #pairs without LUB

12. ep.jhu.edu What do we seek in an agile network 1 AGILE • Knowledge Exchange • Resource Exchange • Reduced Management Overhead • Innovation • Cognitive Diversity • Inclusion • Time (social opportunity) EFFICIENT • Connected • Efficient • Hierarchic • Least Upper Bound It is not clear that Agile is the opposite of Efficient. We wish to maximize connectivity and minimize efficiency and hierarchy.

13. ep.jhu.edu What are we missing? 1 Network “horizons” suggest the likelihood of knowledge/resource exchange between actors approaches 0, as distance >3 Relationships take time and resources How many meaningful conversations? • Software developers 5-6/day • Managers 15-20/day 500-2000 Facebook friends!!!

14. ep.jhu.edu How to Create Truly Agile Networks • Minimize the diameter of the network • Minimize the average degree of actors (a.k.a. density) • Maximize cognitive diversity Diameter = 2 Density = 0.4 Diameter = 1 Density = 1.0 Diameter = 2 Density = 0.5 Need to explore tradeoffs in diameter and density

16. ep.jhu.eduep.jhu.edu Reasoned Action Theory 𝐵 = 𝑤1 𝐴 + 𝑤2 𝐼𝑁 + 𝑤3 𝐷𝑁 ∗ 𝑤4 𝑃𝐵𝐶 • B = Behavior • A = Salient Attitudes • IN = Injunctive Norms • DN = Descriptive Norms • PBC = Perceived Behavioral Control • wi = Weight applied to factor

17. ep.jhu.eduep.jhu.edu Where do you get the data? 𝐵 = 𝑤1 𝐴 + 𝑤2 𝐼𝑁 + 𝑤3 𝐷𝑁 ∗ 𝑤4 𝑃𝐵𝐶 Opinion Leader Key Influencer Key Communic ator Alters Informational Conformity Normative Conformity Network Conformity Social Network Analysis

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20. ep.jhu.eduep.jhu.edu Community Detection • Cohesive clustering – community detection - Newman grouping - Louvain grouping - Truss grouping • Intuitively satisfying clusters • Allows identification of distinct social groups

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22. ep.jhu.eduep.jhu.edu “When is a tourniquet applied to a neck wound?” • When it is a vein or artery. • If it is spurting blood. • Never McCulloh, I. (2013). Social Conformity in Networks. Official Journal of the International Network for Social Network Analysts Network Conformity Experiment

23. ep.jhu.eduep.jhu.edu Centrality

24. ep.jhu.eduep.jhu.edu Degree Keyplayers-Pos.Between Closeness

25. ep.jhu.eduep.jhu.edu Different Leaders for Different Stages 0 20 40 60 80 100 1 2 3 4 5 6 7 8 9 10 PercentAdopters Time Degree Betweenness Closeness

27. ep.jhu.eduep.jhu.edu Social Media Analysis: Structure-Based Analytics Social network construction using relational algebra Let X be an association matrix of screen names by tweetID/image Let Y be an association matrix of tweetID/image by MD5 hash Then, YTXTXY is a hash network of images posted by the same people Pro-ISIS Anti-Assad Shi’a No Confidence 7,887 different hash values 3,583 hash shared by 2+ people

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29. ep.jhu.eduep.jhu.edu Time for the Workshop! 0 500 1000 1500 2013 2014 2015 2016 DailyDownloads Package igraph sna tnet Daily Downloads (RStudio mirror) for igraph, sna, and tnet igraph (also in python) • Social media, cluster, speed statnet • Statistics, longitudinal, egonet Not compatible

30. ep.jhu.eduep.jhu.edu • Centrality measures • Diameter & density • Clustering • Social media context • Network statistics (ERGM, SAOM) What are the most common analytics? It’s the social theory that gives life to analysis! Let’s go to Rstudio! We will use the igraph package Python uses igraph

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